Structural health monitoring (SHM) has evolved over decades of continuous progress in measuring, processing, collecting and storing massive amounts of data that can provide valuable information for owners and managers in order to control and manage the integrity of their structures. The data sets acquired from SHM systems are undoubtedly of the “big data” type due to their sheer volume, complexity and diversity, and conducting relevant analyses of their content can help to identify damage or failure during operation through the relationships between the measurements taken by multiple sensors. A great deal can be learned from these large pools of data, resulting in significant advances in efficient integrity control. From banking to retail, many sectors have already embraced big data, which is often synonymous with “big expectations”; in the present case, it offers opportunities to apply data-processing research to the development of more efficient SHM systems with real-time capabilities. By presenting various examples of bridge monitoring systems, this paper contributes to the ongoing cross-disciplinary efforts in data science for the utilization and advancement of SHM.

Structural health monitoring (SHM) relies on data acquired from sensorial systems installed on site, and is nowadays being used more often not only for asset management, but also in critical structures when there is the need to detect damage in an early stage, before it impairs structural performance and safety. Early detection of damage in critical structures relies on the acquisition of continuous streams of information and on reliable techniques capable of analyzing it in real time, without generating false alerts. In this context, the combination of data fusion strategies, capable of converting large amounts of data into small pieces of information, with pattern recognition algorithms, which are able to analyze this information in real time, is addressed in the present paper with the objective of developing an original strategy capable of (1) removing the effects of regular actions imposed to structures without the need to measure them and of (2) compressing entire SHM data sets of arbitrary dimensions into a sensitive single-valued damage index. These capabilities are achieved by combining principal component analysis, the broke-stick rule, clustering methods, symbolic data objects, and symbolic distances. The proposed strategy was tested and validated with a numerical model of a cable-stayed bridge, using experimental data as input. From this analysis it was observed that, under the noise levels measured on site, the proposed strategy is able to automatically detect damage as small as 1% of stiffness reduction in a single stay cable. This sensitivity evidenced by the proposed strategy can be considered particularly high because it was obtained from a small amount of inexpensive sensors with a static character and because it was associated with a false detection incidence of only 0.1%.

In recent years, the condition of deficient bridges has reached such a level that the volume of required repair actions becomes significant for many countries. As budgets for maintenance, repair and rehabilitation are always limited and demands are constantly increasing, to find an optimal balance between cost and safety is today a new trend in bridge maintenance. Optimising bridge maintenance and management is a strong expectation for owners and stakeholders facing ageing bridge stocks and increasing aggressive traffic. In this context, the assessment of the structural performance may be necessary for various reasons thorough its lifetime. In France, there are no standards or regulations for structural assessment of existing structures. The studies on a new Eurocode standard for the ‘Evaluation and rehabilitation of existing structures’ are just starting and it will be published in several years. For this reason, the French Ministry of Transport has decided to develop recommendations for the assessment of the structural performance of existing bridges. In a first part, the paper summarises today’s practice in France, but it also details the ongoing calibration process for setting appropriate partial factors for existing bridges. Implementation is given as example for the assessment of reinforced concrete slabs.

Three metakaolins are evaluated for use as supplementary cementitious materials in cement-based systems. The metakaolins vary in mineralogical composition and in fabrication (traditional and flash calcination), but are quite similar in their surface area (16–19 m²/g), but are quite similar in mineralogical composition. Performance of metakaolin mixtures will be compared to two control mixtures (standard concrete for foundation C40/50 and high performance concrete C60/75). In this study, the properties of fresh concrete and the mechanical and durability properties of hardened concrete will be examined. The rheological behaviour are aimed to determine the effect of metakaolin on mixture workability. Compressive, tensile and flexural strength and elastic modulus will be measured at various concrete ages. The influence of metakaolin on durability is assessed through rapid chloride migration and carbonation measurements. For high performance concrete mixtures, drying and autogenous shrinkage will be monitored and creep measurements are performed and compared.